$ loading_
Build vector indexes and run semantic search across project files with auto-updates.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "vector-index-mcp" yet — see the docs or source repo.
Use vector-index-mcp to index the current project files, then search for code, configs, and docs related to the 'user login failure retry mechanism'. List the most relevant results by file path with brief notes.
A list of relevant files, why they match, and short summaries of key content.
First index this repository, then answer: what are the core modules of this project and what does each module do? Use semantic search results and cite the main files and directories involved.
A module-level overview of the project with supporting file paths and responsibility notes.
Use vector-index-mcp to watch and index project file updates. After I change API routes or configs, re-run a search for 'payment API timeout handling' and tell me which relevant files have changed.
Updated relevant files from the refreshed index, plus a summary of the impact of the changes.
Build semantic codebase indexes so AI can search and navigate projects faster.
Index source code locally and search it semantically with natural-language queries.
Build semantic indexes for codebases to find relevant code with natural language queries.
Connect AI agents to secure RAG workflows across multiple vector databases.
Index, search, analyze, and monitor codebases for faster understanding and troubleshooting.
Run hybrid search over self-hosted public data indexes via MCP.